Nathaniel Whittemore

He writes, LLMs are trained on sequential reasoning, breaking tasks down step by step, one to do after another.

When you ask them to orchestrate parallel work, they don't know how to split tasks without conflicts.

Moonshot caused this serial collapse and solved it with reinforcement learning.

They used PARL, parallel agent reinforcement learning, where they gave an orchestrator a compute and time budget that made it impossible to complete tasks sequentially.

it was forced to learn how to break tasks down into parallel work for sub-agents to succeed in the environment.

Simon Smith from Qlik Health did a full test as well and came away pretty impressed.

He writes, I've been thinking about the best way to organize agents in step-by-step workflows where each agent has skills defined by an agent skills file and to then scale this across an enterprise.

Today, Kimi dropped its K2.5 model along with agent swarms and I thought, could this be it?

The answer?

Mostly.

He then walks through how you do this.

First, using Kimi, you actually use the model selector to select Agent Swarm in the same way that you would select between, for example, instant or thinking mode.

For Simon's task, he gave Agent Swarm the task of responding to an RFP, which included, in his words, research, strategy, creative brainstorming, and concept development, media planning, analytics planning, high-level project planning, and consolidating everything into a final written response in a Word document.

He continues, as would be familiar to users of agentic coding tools like Cloud Code and Codex, Kimi turns your request into a step-by-step plan and then proceeds to work through it.

Where things get interesting, however, is how it executes the plan with multiple agents.

For each step in the plan, he writes, Kimi creates a set of relevant agents.

And importantly, these aren't generic agents.

Agents each have roles and names.

Each agent, he writes, plays a specific role, defined for it in a prompt, and even gets a name and avatar.

The role description ensures the agent focuses on a specific job to be done, and the name and avatar make this extremely user-friendly.